This is the electronic supplemental data for the publication entitled "Phenomenology of Avalanche Recordings from Distributed Acoustic Sensing" submitted to the Journal of Geophysical Research (JGR): Earth Surface. The main_jupyter_notebook.ipynb shows an example workflow on how to read the data and utilize the Bayesian Gaussian Mixture Model on the extracted features to predict different classes (/clusters) within the avalanche recordings. The pre-print will be available on ESSOAr (currently processing submission, as of Dec1., 2022): https://doi.org/10.1002/essoar.10512949.1 Requirements Code was written in python 3.9.13 (from conda-forge), and the following packages are required (the version in the brackets are for which the code was tested): jupyter (versions see below) jupyter 1.0.0 jupyter_client 7.3.5 jupyter_console 6.4.3 jupyter_core 4.11.2 jupyter_server 1.18.1 jupyterlab 3.4.4 jupyterlab_pygments 0.1.2 jupyterlab_server 2.15.2 jupyterlab_widgets 1.0.0 numpy (1.23.3) pandas (1.4.4) scipy (1.9.3) matplotlib (versions see below) matplotlib-base 3.5.2 matplotlib-inline 0.1.6 cmocean (2.0 from channel conda-forge) sklearn (scikit-learn) (1.1.3)

Funding Acknowledgements: - ETH Zurich: ETH-01 16-2 (Patrick Paitz) - Swiss National Science Foundation: CRSK-2_190683 (Fabian Walter) - Swiss National Science Foundation: PP00P2_157551/2 (Fabian Walter) - Swiss National Science Foundation: 206021_113069/1 (Betty Sovilla)